Knowledge Representation with predicate knoledgeFirstOrderLogic.ppt

Knowledge representation with first order logic and predicate logic

1. Predicate Logic
First-Order Logic

2. Outline
•First-order logic
–Properties, relations, functions, quantifiers, …
–Terms, sentences, axioms, theories, proofs, …
•Extensions to first-order logic
•Logical agents
–Reflex agents
–Representing change: situation calculus, frame problem
–Preferences on actions
–Goal-based agents

4. Predicate Logic
•Predicate logic is an extension of propositional logic.
•A= The Ball’s color is Red ( propositional)
•Color(Ball,Red) Predicate is Color and Ball and Red are
argument.
•Rohan Likes Banana
•Likes(Rohan, Banana)
•Where likes is a predicate and rohanand banana are
argument

5. Predicate Logic
•Quantifies : We need include all and some
•Universal
∀
•Existential ∃
•Everybody loves somebody
•∀x∃y
L(x,y)=loves(everybody,somebody)

6. First-order logic
•First-order logic (FOL) models the world in terms of
–Objects,which are things with individual identities
–Propertiesof objects that distinguish them from other objects
–Relationsthat hold among sets of objects
–Functions,which are a subset of relations where there is only one
“value” for any given “input”
•Examples:
–Objects: Students, lectures, companies, cars ...
–Relations: Brother-of, bigger-than, outside, part-of, has-color,
occurs-after, owns, visits, precedes, ...
–Properties: blue, oval, even, large, ...
–Functions: father-of, best-friend, second-half, one-more-than ...

10. User provides
•Constant symbols,which represent individuals in the world
–Mary
–3
–Green
•Function symbols,which map individuals to individuals
–father-of(Mary) = John
–color-of(Sky) = Blue
•Predicate symbols,which map individuals to truth values
–greater(5,3)
–green(Grass)
–color(Grass, Green)

11. FOL Provides
•Variable symbols
–E.g., x, y, foo
•Connectives
–Same as in PL: not (), and (), or (), implies (), if
and only if (biconditional )
•Quantifiers
–Universal xor (Ax)
–Existential xor (Ex)

12. Sentences are built from terms and atoms
•A term(denoting a real-world individual) is a constant symbol, a
variable symbol, or an n-place function of n terms.
x and f(x
1, ..., x
n) are terms, where each x
iis a term.
A term with no variables is a ground term
•An atomic sentence(which has value true or false) is an n-place
predicate of n terms
•A complex sentenceis formed from atomic sentences connected
by the logical connectives:
P, PQ, PQ, PQ, PQ where P and Q are sentences
•A quantified sentenceadds quantifiers and 
•A well-formed formula(wff)is a sentence containing no “free”
variables. That is, all variables are “bound” by universal or
existential quantifiers.
(x)P(x,y) has x bound as a universally quantified variable, but y is free.

13. Quantifiers
•Universalquantification
–(x)P(x) means that P holds for allvalues of x in the
domain associated with that variable
–E.g., (x) dolphin(x) mammal(x)
•Existentialquantification
–(x)P(x) means that P holds for somevalue of x in the
domain associated with that variable
–E.g., (x) mammal(x) lays-eggs(x)
–Permits one to make a statement about some object
without naming it

14. Quantifiers
•Universal quantifiers are often used with “implies” to form “rules”:
(x) student(x) smart(x) means “All students are smart”
•Universal quantification is rarely used to make blanket statements
about every individual in the world:
(x)student(x)smart(x) means “Everyone in the world is a student and is smart”
•Existential quantifiers are usually used with “and” to specify a list of
properties about an individual:
(x) student(x) smart(x) means “There is a student who is smart”
•A common mistake is to represent this English sentence as the FOL
sentence:
(x) student(x) smart(x)
–But what happens when there is a person who is nota student?

15. Quantifier Scope
•Switching the order of universal quantifiers does notchange
the meaning:
–(x)(y)P(x,y) ↔(y)(x) P(x,y)
•Similarly, you can switch the order of existential
quantifiers:
–(x)(y)P(x,y) ↔(y)(x) P(x,y)
•Switching the order of universals and existentials does
change meaning:
–Everyone likes someone: (x)(y) likes(x,y)
–Someone is liked by everyone: (y)(x) likes(x,y)

16. Connections between All and Exists
We can relate sentences involving and 
using De Morgan’s laws:
(x) P(x) ↔(x) P(x)
(x) P ↔(x) P(x)
(x) P(x) ↔(x) P(x)
(x) P(x) ↔(x) P(x)

17. Quantified inference rules
•Universal instantiation
–x P(x) P(A)
•Universal generalization
–P(A) P(B) … x P(x)
•Existential instantiation
–x P(x) P(F) skolem constant F
•Existential generalization
–P(A) x P(x)

18. Universal instantiation
(a.k.a. universal elimination)
•If (x) P(x) is true, then P(C) is true, where C is any
constant in the domain of x
•Example:
(x) eats(Ziggy, x) eats(Ziggy, IceCream)
•The variable symbol can be replaced by any ground term,
i.e., any constant symbol or function symbol applied to
ground terms only

19. Existential instantiation
(a.k.a. existential elimination)
•From (x) P(x) infer P(c)
•Example:
–(x) eats(Ziggy, x) eats(Ziggy, Stuff)
•Note that the variable is replaced by a brand-new constant
not occurring in this or any other sentence in the KB
•Also known as skolemization; constant is a skolem
constant
•In other words, we don’t want to accidentally draw other
inferences about it by introducing the constant
•Convenient to use this to reason about the unknown object,
rather than constantly manipulating the existential quantifier

20. Existential generalization
(a.k.a. existential introduction)
•If P(c) is true, then (x) P(x) is inferred.
•Example
eats(Ziggy, IceCream) (x) eats(Ziggy, x)
•All instances of the given constant symbol are replaced by
the new variable symbol
•Note that the variable symbol cannot already exist
anywhere in the expression

21. Translating English to FOL
Every gardener likes the sun.
x gardener(x) likes(x,Sun)
You can fool some of the people all of the time.
x t person(x) time(t) can-fool(x,t)
You can fool all of the people some of the time.
x t (person(x) time(t) can-fool(x,t))
x (person(x) t (time(t) can-fool(x,t))
All purple mushrooms are poisonous.
x (mushroom(x) purple(x)) poisonous(x)
No purple mushroom is poisonous.
x purple(x) mushroom(x) poisonous(x)
x (mushroom(x) purple(x)) poisonous(x)
There are exactly two purple mushrooms.
x y mushroom(x) purple(x) mushroom(y) purple(y) ^ (x=y) z
(mushroom(z) purple(z)) ((x=z) (y=z))
Clinton is not tall.
tall(Clinton)
X is above Y iff X is on directly on top of Y or there is a pile of one or more other
objects directly on top of one another starting with X and ending with Y.
x y above(x,y) ↔(on(x,y) z (on(x,z) above(z,y)))
Equivalent
Equivalent

22. Monty Python and The Art of Fallacy
Cast
–Sir Bedevere the Wise, master of (odd) logic
–King Arthur
–Villager 1, witch-hunter
–Villager 2, ex-newt
–Villager 3, one-line wonder
–All, the rest of you scoundrels, mongrels, and
nere-do-wells.

23. An example from Monty Python
by way of Russell & Norvig
•FIRST VILLAGER:We have found a witch. May we burn
her?
•ALL: A witch! Burn her!
•BEDEVERE:Why do you think she is a witch?
•SECOND VILLAGER: She turned meinto a newt.
•B: A newt?
•V2(after looking at himself for some time): I got better.
•ALL:Burn her anyway.
•B: Quiet! Quiet! There are ways of telling whether she is a
witch.

24. Monty Python cont.
•B:Tell me… what do you do with witches?
•ALL: Burn them!
•B:And what do you burn, apart from witches?
•Third Villager:…wood?
•B:So why do witches burn?
•V2(after a beat): because they’re made of wood?
•B: Good.
•ALL: I see. Yes, of course.

25. Monty Python cont.
•B: So how can we tell if she is made of wood?
•V1: Make a bridge out of her.
•B: Ah… but can you not also make bridges out of stone?
•ALL:Yes, of course… um… er…
•B: Does wood sink in water?
•ALL: No, no, it floats. Throw her in the pond.
•B: Wait. Wait… tell me, what also floats on water?
•ALL:Bread? No, no no. Apples… gravy… very small
rocks…
•B: No, no, no,

26. Monty Python cont.
•KING ARTHUR:A duck!
•(They all turn and look at Arthur. Bedevere looks up, very
impressed.)
•B: Exactly. So… logically…
•V1 (beginning to pick up the thread): If she… weighs the
same as a duck… she’s made of wood.
•B:And therefore?
•ALL: A witch!

27. Monty Python Fallacy #1
•x witch(x) burns(x)
•x wood(x) burns(x)
•-------------------------------
•z witch(x) wood(x)
•p q
•r q
•---------
•p r Fallacy: Affirming the conclusion

28. Monty Python Near-Fallacy #2
•wood(x) can-build-bridge(x)
•-----------------------------------------
•can-build-bridge(x) wood(x)
•B: Ah… but can you not also make bridges out of stone?

29. Monty Python Fallacy #3
•x wood(x) floats(x)
•x duck-weight (x) floats(x)
•-------------------------------
•x duck-weight(x) wood(x)
•p q
•r q
•-----------
•r p

30. Monty Python Fallacy #4
•z light(z) wood(z)
•light(W)
•------------------------------
•wood(W) ok…………..
•witch(W) wood(W) applying universal instan.
to fallacious conclusion #1
•wood(W)
•---------------------------------
•witch(z)

31. Example: A simple genealogy KB by FOL
•Build a small genealogy knowledge base using FOL that
–contains facts of immediate family relations (spouses, parents, etc.)
–contains definitions of more complex relations (ancestors, relatives)
–is able to answer queries about relationships between people
•Predicates:
–parent(x, y), child(x, y), father(x, y), daughter(x, y), etc.
–spouse(x, y), husband(x, y), wife(x,y)
–ancestor(x, y), descendant(x, y)
–male(x), female(y)
–relative(x, y)
•Facts:
–husband(Joe, Mary), son(Fred, Joe)
–spouse(John, Nancy), male(John), son(Mark, Nancy)
–father(Jack, Nancy), daughter(Linda, Jack)
–daughter(Liz, Linda)
–etc.

32. •Rules for genealogical relations
–(x,y) parent(x, y) ↔child (y, x)
(x,y) father(x, y) ↔parent(x, y) male(x) (similarly for mother(x, y))
(x,y) daughter(x, y) ↔child(x, y) female(x) (similarly for son(x, y))
–(x,y) husband(x, y) ↔spouse(x, y) male(x) (similarly for wife(x, y))
(x,y) spouse(x, y) ↔spouse(y, x) (spouse relation is symmetric)
–(x,y) parent(x, y) ancestor(x, y)
(x,y)(z) parent(x, z) ancestor(z, y) ancestor(x, y)
–(x,y) descendant(x, y) ↔ancestor(y, x)
–(x,y)(z) ancestor(z, x) ancestor(z, y) relative(x, y)
(related by common ancestry)
(x,y) spouse(x, y) relative(x, y) (related by marriage)
(x,y)(z) relative(z, x) relative(z, y) relative(x, y) (transitive)
(x,y) relative(x, y) ↔relative(y, x) (symmetric)
•Queries
–ancestor(Jack, Fred) /* the answer is yes */
–relative(Liz, Joe) /* the answer is yes */
–relative(Nancy, Matthew)
/* no answer in general, no if under closed world assumption */
–(z) ancestor(z, Fred) ancestor(z, Liz)

33. Semantics of FOL
•Domain M: the set of all objects in the world (of interest)
•Interpretation I: includes
–Assign each constant to an object in M
–Define each function of n arguments as a mapping M
n
=> M
–Define each predicate of n arguments as a mapping M
n
=> {T, F}
–Therefore, every ground predicate with any instantiation will have a
truth value
–In general there is an infinite number of interpretations because |M| is
infinite
•Define logical connectives: ~, ^, , =>, <=>as in PL
•Define semantics of (x) and (x)
–(x) P(x) is true iff P(x) is true under all interpretations
–(x) P(x) is true iff P(x) is true under some interpretation

34. •Model: an interpretation of a set of sentences such that every
sentence is True
•A sentence is
–satisfiableif it is true under some interpretation
–valid if it is true under all possible interpretations
–inconsistentif there does not exist any interpretation under which the
sentence is true
•Logical consequence: S |= X if all models of S are also
models of X

35. Axioms, definitions and theorems
•Axiomsare facts and rules that attempt to capture all of the
(important) facts and concepts about a domain; axioms can
be used to prove theorems
–Mathematicians don’t want any unnecessary (dependent) axioms –ones
that can be derived from other axioms
–Dependent axioms can make reasoning faster, however
–Choosing a good set of axioms for a domain is a kind of design
problem
•A definitionof a predicate is of the form “p(X) ↔…” and
can be decomposed into two parts
–Necessarydescription: “p(x) …”
–Sufficientdescription “p(x) …”
–Some concepts don’t have complete definitions (e.g., person(x))

36. More on definitions
•A necessarycondition must be satisfied for a statement to be true.
•A sufficientcondition, if satisfied, assures the statement’s truth.
•Duality: “P is sufficient for Q” is the same as “Q is necessary for P.”
•Examples: define father(x, y) by parent(x, y) and male(x)
–parent(x, y) is a necessary (but not sufficient) description of
father(x, y)
•father(x, y) parent(x, y)
–parent(x, y) ^ male(x) ^ age(x, 35) is a sufficient(but not necessary)
description of father(x, y):
father(x, y) parent(x, y) ^ male(x) ^ age(x, 35)
–parent(x, y) ^ male(x) is a necessary and sufficientdescription of
father(x, y)
parent(x, y) ^ male(x) ↔father(x, y)

37. More on definitions
P(x)
S(x)
S(x) is a
necessary
condition of P(x)
(x) P(x) => S(x)
S(x)
P(x)
S(x) is a
sufficient
condition of P(x)
(x) P(x) <= S(x)
P(x)
S(x)
S(x) is a
necessary and
sufficient
condition of P(x)
(x) P(x) <=> S(x)

38. Higher-order logic
•FOL only allows to quantify over variables, and variables
can only range over objects.
•HOL allows us to quantify over relations
•Example: (quantify over functions)
“two functions are equal iff they produce the same value for all
arguments”
f g (f = g) (x f(x) = g(x))
•Example: (quantify over predicates)
r transitive( r ) (xyz) r(x,y) r(y,z) r(x,z))
•More expressive, but undecidable. (there isn’t an effective
algorithm to decide whether all sentences are valid)
–First-order logic is decidable only when it uses predicates with only one
argument.

39. Expressing uniqueness
•Sometimes we want to say that there is a single, unique
object that satisfies a certain condition
•“There exists a unique x such that king(x) is true”
–x king(x) y (king(y) x=y)
–x king(x) y (king(y) xy)
–! x king(x)
•“Every country has exactly one ruler”
–c country(c) ! r ruler(c,r)
•Iota operator: “x P(x)” means “the unique x such that p(x)
is true”
–“The unique ruler of Freedonia is dead”
–dead(x ruler(freedonia,x))

40. Notational differences
•Different symbolsfor and, or, not, implies, ...
–
–p v (q ^ r)
–p + (q * r)
–etc
•Prolog
cat(X) :-furry(X), meows (X), has(X, claws)
•Lispy notations
(forall ?x (implies (and (furry ?x)
(meows ?x)
(has ?x claws))
(cat ?x)))

41. Logical agents for the Wumpus World
Three (non-exclusive) agent architectures:
–Reflex agents
•Have rules that classify situations, specifying how to
react to each possible situation
–Model-based agents
•Construct an internal model of their world
–Goal-based agents
•Form goals and try to achieve them

42. A simple reflex agent
•Rules to map percepts into observations:
b,g,u,c,t Percept([Stench, b, g, u, c], t) Stench(t)
s,g,u,c,t Percept([s, Breeze, g, u, c], t) Breeze(t)
s,b,u,c,t Percept([s, b, Glitter, u, c], t) AtGold(t)
•Rules to select an action given observations:
t AtGold(t) Action(Grab, t);
•Some difficulties:
–Consider Climb. There is no percept that indicates the agent should
climb out –position and holding gold are not part of the percept
sequence
–Loops –the percept will be repeated when you return to a square,
which should cause the same response (unless we maintain some
internal model of the world)

43. Representing change
•Representing change in the world in logic can be
tricky.
•One way is just to change the KB
–Add and delete sentences from the KB to reflect changes
–How do we remember the past, or reason about changes?
•Situation calculusis another way
•A situationis a snapshot of the world at some
instant in time
•When the agent performs an action A in
situation S1, the result is a new situation
S2.

44. Situations

45. Situation calculus
•A situationis a snapshot of the world at an interval of time during which
nothing changes
•Every true or false statement is made with respect to a particular situation.
–Add situation variablesto every predicate.
–at(Agent,1,1) becomesat(Agent,1,1,s0): at(Agent,1,1) is true in situation (i.e., state)
s0.
–Alternatively, add a special 2
nd
-order predicate, holds(f,s),that means “f is true in
situation s.” E.g., holds(at(Agent,1,1),s0)
•Add a new function, result(a,s),that maps a situation s into a new situation as a
result of performing action a. For example, result(forward, s) is a function that
returns the successor state (situation) to s
•Example: The action agent-walks-to-location-y could be represented by
–(x)(y)(s) (at(Agent,x,s) onbox(s)) at(Agent,y,result(walk(y),s))

46. Deducing hidden properties
•From the perceptual information we obtain in situations, we
can infer properties of locations
l,s at(Agent,l,s) Breeze(s) Breezy(l)
l,s at(Agent,l,s) Stench(s) Smelly(l)
•Neither Breezy nor Smelly need situation arguments
because pits and Wumpuses do not move around

47. Deducing hidden properties II
•We need to write some rules that relate various aspects of a
single world state (as opposed to across states)
•There are two main kinds of such rules:
–Causal rulesreflect the assumed direction of causality in the world:
(l1,l2,s) At(Wumpus,l1,s) Adjacent(l1,l2) Smelly(l2)
(l1,l2,s) At(Pit,l1,s) Adjacent(l1,l2) Breezy(l2)
Systems that reason with causal rules are called model-based
reasoning systems
–Diagnostic rulesinfer the presence of hidden propertiesdirectly
from the percept-derived information. We have already seen two
diagnostic rules:
(l,s) At(Agent,l,s) Breeze(s) Breezy(l)
(l,s) At(Agent,l,s) Stench(s) Smelly(l)

48. Representing change:
The frame problem
•Frame axioms: If property x doesn’t change as a result of
applying action a in state s, then it stays the same.
–On (x, z, s) Clear (x, s) 
On (x, table, Result(Move(x, table), s)) 
On(x, z, Result (Move (x, table), s))
–On (y, z, s) yx On (y, z, Result (Move (x, table), s))
–The proliferation of frame axioms becomes very cumbersome in
complex domains

49. The frame problem II
•Successor-state axiom: General statement that
characterizes every way in which a particular predicate can
become true:
–Either it can be madetrue, or it can already be true and not be
changed:
–On (x, table, Result(a,s)) 
[On (x, z, s) Clear (x, s) a = Move(x, table)] 
[On (x, table, s) a Move (x, z)]
•In complex worlds, where you want to reason about longer
chains of action, even these types of axioms are too
cumbersome
–Planning systems use special-purpose inference methods to reason
about the expected state of the world at any point in time during a
multi-step plan

50. Qualification problem
•Qualification problem:
–How can you possibly characterize every single effect of an action,
or every single exception that might occur?
–When I put my bread into the toaster, and push the button, it will
become toasted after two minutes, unless…
•The toaster is broken, or…
•The power is out, or…
•I blow a fuse, or…
•A neutron bomb explodes nearby and fries all electrical components,
or…
•A meteor strikes the earth, and the world we know it ceases to exist,
or…

51. Ramification problem
•Similarly, it’s just about impossible to characterize every side effect of
every action, at every possible level of detail:
–When I put my bread into the toaster, and push the button, the bread will
become toasted after two minutes, and…
•The crumbs that fall off the bread onto the bottom of the toaster over tray will
also become toasted, and…
•Some of the aforementioned crumbs will become burnt, and…
•The outside molecules of the bread will become “toasted,” and…
•The inside molecules of the bread will remain more “breadlike,” and…
•The toasting process will release a small amount of humidity into the air because
of evaporation, and…
•The heating elements will become a tiny fraction more likely to burn out the next
time I use the toaster, and…
•The electricity meter in the house will move up slightly, and…

52. Knowledge engineering!
•Modeling the “right” conditions and the “right” effects at
the “right” level of abstraction is very difficult
•Knowledge engineering (creating and maintaining
knowledge bases for intelligent reasoning) is an entire field
of investigation
•Many researchers hope that automated knowledge
acquisition and machine learning tools can fill the gap:
–Our intelligent systems should be able to learnabout the conditions
and effects, just like we do!
–Our intelligent systems should be able to learn when to pay attention
to, or reason about, certain aspects of processes, depending on the
context!

53. Preferences among actions
•A problem with the Wumpus world knowledge base that we
have built so far is that it is difficult to decide which action
is best among a number of possibilities.
•For example, to decide between a forward and a grab,
axioms describing when it is OK to move to a square would
have to mention glitter.
•This is not modular!
•We can solve this problem by separating facts about
actions from facts about goals. This way our agent can be
reprogrammed just by asking it to achieve different
goals.

54. Preferences among actions
•The first step is to describe the desirability of actions
independent of each other.
•In doing this we will use a simple scale: actions can be
Great, Good, Medium, Risky, or Deadly.
•Obviously, the agent should always do the best action it can
find:
(a,s) Great(a,s) Action(a,s)
(a,s) Good(a,s) (b) Great(b,s) Action(a,s)
(a,s) Medium(a,s) ((b) Great(b,s) Good(b,s)) Action(a,s)
...

55. Preferences among actions
•We use this action quality scale in the following way.
•Until it finds the gold, the basic strategy for our agent is:
–Great actions include picking up the gold when found and climbing
out of the cave with the gold.
–Good actions include moving to a square that’s OK and hasn't been
visited yet.
–Medium actions include moving to a square that is OK and has
already been visited.
–Risky actions include moving to a square that is not known to be
deadly or OK.
–Deadly actions are moving into a square that is known to have a pit
or a Wumpus.

56. Goal-based agents
•Once the gold is found, it is necessary to change strategies.
So now we need a new set of action values.
•We could encode this as a rule:
–(s) Holding(Gold,s) GoalLocation([1,1]),s)
•We must now decide how the agent will work out a
sequence of actions to accomplish the goal.
•Three possible approaches are:
–Inference: good versus wasteful solutions
–Search: make a problem with operators and set of states
–Planning: to be discussed later